Pub Date : 2023-04-03DOI: 10.1080/19475683.2023.2182360
Min Li, Wen Dai, Susu Song, C. Wang, Yu Tao
ABSTRACT High-precision digital elevation models (DEMs) are the basic data for constructing digital cities. With the acceleration of urbanization, the topography of urban plots is constantly transformed by human activities, so that surface morphology shows the characteristics of diversification and discontinuity. Existing modelling methods focus on the expression of continuous terrain. Constructing high-precision DEM in urban plots is still challenging. A block-based modelling method that considers the morphological characteristics of urban plot elements was proposed. The Jinzhai County urban area was selected as the research area. The elements of urban plots were classified into six types, in which the boundaries and elevation information were extracted with real sense 3D models, Digital Orthophoto Maps (DOMs) and dense matching point cloud data. The DEMs of the 6 types of elements were generated separately with different methods, that are fused to complete the DEM of urban plots. The DEM obtained using the method in this manuscript was consistent with the reality in terms of topographic relief within each element and clarified the boundary of each element. The accuracy assessment showed that the RMSE results of roads, slopes, other terrains and natural terrains are approximately 0.05 m, which meets the elevation accuracy requirements of the 1:1000 large-scale mapping.
{"title":"Construction of high-precision DEMs for urban plots","authors":"Min Li, Wen Dai, Susu Song, C. Wang, Yu Tao","doi":"10.1080/19475683.2023.2182360","DOIUrl":"https://doi.org/10.1080/19475683.2023.2182360","url":null,"abstract":"ABSTRACT High-precision digital elevation models (DEMs) are the basic data for constructing digital cities. With the acceleration of urbanization, the topography of urban plots is constantly transformed by human activities, so that surface morphology shows the characteristics of diversification and discontinuity. Existing modelling methods focus on the expression of continuous terrain. Constructing high-precision DEM in urban plots is still challenging. A block-based modelling method that considers the morphological characteristics of urban plot elements was proposed. The Jinzhai County urban area was selected as the research area. The elements of urban plots were classified into six types, in which the boundaries and elevation information were extracted with real sense 3D models, Digital Orthophoto Maps (DOMs) and dense matching point cloud data. The DEMs of the 6 types of elements were generated separately with different methods, that are fused to complete the DEM of urban plots. The DEM obtained using the method in this manuscript was consistent with the reality in terms of topographic relief within each element and clarified the boundary of each element. The accuracy assessment showed that the RMSE results of roads, slopes, other terrains and natural terrains are approximately 0.05 m, which meets the elevation accuracy requirements of the 1:1000 large-scale mapping.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"30 1","pages":"193 - 203"},"PeriodicalIF":5.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78757657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-24DOI: 10.1080/19475683.2023.2192761
Wendy L. Zeller Zigaitis, A. Robinson
ABSTRACT A key challenge for mitigating poaching within protected areas is to understand the geospatial data that are collected by practitioners in protected areas and to characterize the ability to synthesize those data with landscape-level data to form a holistic picture of the movement patterns of humans and animals. Literature reviewed from the past 15 years on geospatial data collected by practitioners to mitigate wildlife poaching reveals a gap in our knowledge on how protected area practitioners make sense of geospatial data that are collected within protected areas. Geospatial data collected within protected areas provide an understanding of movement patterns of humans and animals, which can provide insight on best practices for poaching mitigation, to include where to emplace new geospatial sensors. We classify these data as device-based and human-generated, and their potential to provide geospatially referenced information that forms patterns of poaching activity. This article examines two primary types of geospatial data collected in protected areas, highlights the challenges associated with this data, and discusses knowledge gaps regarding how protected areas make sense of spatial data. We conclude with recommendations for future research on characterizing how geospatial data is represented in protected areas, and filling knowledge gaps on how protected area personnel use those data.
{"title":"Sensemaking in the Wild: A Review of Practitioner Collected Geospatial Data and its Synthesis within Protected Areas for Poaching Mitigation","authors":"Wendy L. Zeller Zigaitis, A. Robinson","doi":"10.1080/19475683.2023.2192761","DOIUrl":"https://doi.org/10.1080/19475683.2023.2192761","url":null,"abstract":"ABSTRACT A key challenge for mitigating poaching within protected areas is to understand the geospatial data that are collected by practitioners in protected areas and to characterize the ability to synthesize those data with landscape-level data to form a holistic picture of the movement patterns of humans and animals. Literature reviewed from the past 15 years on geospatial data collected by practitioners to mitigate wildlife poaching reveals a gap in our knowledge on how protected area practitioners make sense of geospatial data that are collected within protected areas. Geospatial data collected within protected areas provide an understanding of movement patterns of humans and animals, which can provide insight on best practices for poaching mitigation, to include where to emplace new geospatial sensors. We classify these data as device-based and human-generated, and their potential to provide geospatially referenced information that forms patterns of poaching activity. This article examines two primary types of geospatial data collected in protected areas, highlights the challenges associated with this data, and discusses knowledge gaps regarding how protected areas make sense of spatial data. We conclude with recommendations for future research on characterizing how geospatial data is represented in protected areas, and filling knowledge gaps on how protected area personnel use those data.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"5 1","pages":"319 - 335"},"PeriodicalIF":5.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84684952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-19DOI: 10.1080/19475683.2023.2192768
Rachid Harroucha, Abdel-Ali Chaouni
ABSTRACT The acceleration of urbanization leads to a geographical extension of cities, longer distances and travel times, increased motorization, and a constantly growing population. Faced with this situation, new urban policies are attempting to control the interaction between urban planning and urban mobility and to redefine a new dimension of proximity, which reduces excessive use of energy-consuming and polluting modes of travel, namely vehicles. This orientation is embodied in the 15-minute city model, which focuses on pedestrian accessibility from one’s home to nearby services and urban spaces. From this perspective, the paper proposes a GIS-based model to evaluate pedestrian accessibility to mosques. Using the open-source software QGIS, a total number of 30 mosques in the Agdal district in the city of Fez in Morocco were studied to investigate their pedestrian accessibility, by examining their spatial distribution and also their capacity to accommodate the worshippers of their catchment areas. Considering the land use and the population density of each mosque’s catchment area, the results showed that even if a mosque can be spatially accessible by walking, it may not be able to comfortably satisfy the potential worshippers in its area of influence. Many people are then forced to travel long distances, sometimes by vehicles, to reach other mosques that can eventually accommodate them. The proposed method helps city planners better understand the urban configuration in terms of spatial and functional pedestrian accessibility, for more inclusive and equitable cities.
{"title":"GIS-based approach evaluating sustainable spatio-functional accessibility to mosques","authors":"Rachid Harroucha, Abdel-Ali Chaouni","doi":"10.1080/19475683.2023.2192768","DOIUrl":"https://doi.org/10.1080/19475683.2023.2192768","url":null,"abstract":"ABSTRACT The acceleration of urbanization leads to a geographical extension of cities, longer distances and travel times, increased motorization, and a constantly growing population. Faced with this situation, new urban policies are attempting to control the interaction between urban planning and urban mobility and to redefine a new dimension of proximity, which reduces excessive use of energy-consuming and polluting modes of travel, namely vehicles. This orientation is embodied in the 15-minute city model, which focuses on pedestrian accessibility from one’s home to nearby services and urban spaces. From this perspective, the paper proposes a GIS-based model to evaluate pedestrian accessibility to mosques. Using the open-source software QGIS, a total number of 30 mosques in the Agdal district in the city of Fez in Morocco were studied to investigate their pedestrian accessibility, by examining their spatial distribution and also their capacity to accommodate the worshippers of their catchment areas. Considering the land use and the population density of each mosque’s catchment area, the results showed that even if a mosque can be spatially accessible by walking, it may not be able to comfortably satisfy the potential worshippers in its area of influence. Many people are then forced to travel long distances, sometimes by vehicles, to reach other mosques that can eventually accommodate them. The proposed method helps city planners better understand the urban configuration in terms of spatial and functional pedestrian accessibility, for more inclusive and equitable cities.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"8 1","pages":"429 - 439"},"PeriodicalIF":5.0,"publicationDate":"2023-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90418365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-09DOI: 10.1080/19475683.2023.2181394
Stuti Ahuja, Sonali Patil, Ujwala M. Bhangale
ABSTRACT Semantic understanding of high spatial resolution remote sensing (RS) images can be divided into object detection, object labelling, identification of geospatial relationships, and semantic description generation. Geographical relations represent the spatial distribution dependencies between geospatial entities such as points, lines, and polygons, and the topologies among them. Geospatial relations play a very important role in describing the relations between geographic objects. These relations can be broadly classified as topological, directional, and proximity relations. These relations describe the adjacency and association relations between geospatial objects. An approach to identify an appropriate directional geospatial relationship between geo-objects present in high spatial resolution RS images is proposed in this paper. Geospatial objects in the form of the closed boundary are taken as input and relationship triplets are generated. Two approaches have been used in the identification of directional relationships and the results of both approaches are compared. The first approach is based on the centroid of the objects and the second considers whole objects while calculating the direction. These relations are then further represented using a knowledge graph, where nodes represent objects and edges represent their relationship. Knowledge graph plays a very important role in overall scene understanding. It shows the association of all objects with each other. These relationships are then represented in the form of descriptions by using template-based sentence generation. Results show that these directional relationships are accurately identified between each pair of objects using both approaches, but relations generated by considering whole objects are closer to human cognition. Semantic understanding of remote sensing images is of great significance in different applications such as urban surveys, urban planning, and management, military intelligence, etc.
{"title":"Semantic understanding of high spatial resolution remote sensing images using directional geospatial relationships","authors":"Stuti Ahuja, Sonali Patil, Ujwala M. Bhangale","doi":"10.1080/19475683.2023.2181394","DOIUrl":"https://doi.org/10.1080/19475683.2023.2181394","url":null,"abstract":"ABSTRACT Semantic understanding of high spatial resolution remote sensing (RS) images can be divided into object detection, object labelling, identification of geospatial relationships, and semantic description generation. Geographical relations represent the spatial distribution dependencies between geospatial entities such as points, lines, and polygons, and the topologies among them. Geospatial relations play a very important role in describing the relations between geographic objects. These relations can be broadly classified as topological, directional, and proximity relations. These relations describe the adjacency and association relations between geospatial objects. An approach to identify an appropriate directional geospatial relationship between geo-objects present in high spatial resolution RS images is proposed in this paper. Geospatial objects in the form of the closed boundary are taken as input and relationship triplets are generated. Two approaches have been used in the identification of directional relationships and the results of both approaches are compared. The first approach is based on the centroid of the objects and the second considers whole objects while calculating the direction. These relations are then further represented using a knowledge graph, where nodes represent objects and edges represent their relationship. Knowledge graph plays a very important role in overall scene understanding. It shows the association of all objects with each other. These relationships are then represented in the form of descriptions by using template-based sentence generation. Results show that these directional relationships are accurately identified between each pair of objects using both approaches, but relations generated by considering whole objects are closer to human cognition. Semantic understanding of remote sensing images is of great significance in different applications such as urban surveys, urban planning, and management, military intelligence, etc.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"8 1","pages":"401 - 414"},"PeriodicalIF":5.0,"publicationDate":"2023-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88362831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-02DOI: 10.1080/19475683.2023.2186487
Qinjun Qiu, Zhong Xie, K. Ma, Miao Tian
ABSTRACT Unlike alphabet-based languages such as English, the Chinese language has no specifying word boundaries. Segmentation, particularly for the Chinese language, is a fundamental step towards Chinese text processing, information retrieval, and knowledge discovery. In the geoscience domain, most existing Chinese word segmentation tools/models require a prespecified dictionary and a large amount of relevant training corpus, and the segmentation accuracies drop significantly when processing out-domain situations using these same methods. To address this issue, a purely unsupervised and generic two-stage architecture (named BERTCWS) for domain-specific Chinese word segmentation is proposed. We first design an incidence matrix termed the ‘character combination tightness’ to calculate the closeness between characters. Then, BERTCWS recognizes geoscience terms based on a Bidirectional Encoder Representations from Transformers(BERT)-based segmenter, and multi-granular segmentation is generated by setting different thresholds. Finally, the discriminator is constructed to validate the correctness of the segmented words. Our numerical study demonstrates that BERTCWS can identify both general-domain terms and geoscience-domain terms. Additionally, multi-granular segmentation could be applied to offer a set of potential geoscience terms of various lengths.
{"title":"BERTCWS: unsupervised multi-granular Chinese word segmentation based on a BERT method for the geoscience domain","authors":"Qinjun Qiu, Zhong Xie, K. Ma, Miao Tian","doi":"10.1080/19475683.2023.2186487","DOIUrl":"https://doi.org/10.1080/19475683.2023.2186487","url":null,"abstract":"ABSTRACT Unlike alphabet-based languages such as English, the Chinese language has no specifying word boundaries. Segmentation, particularly for the Chinese language, is a fundamental step towards Chinese text processing, information retrieval, and knowledge discovery. In the geoscience domain, most existing Chinese word segmentation tools/models require a prespecified dictionary and a large amount of relevant training corpus, and the segmentation accuracies drop significantly when processing out-domain situations using these same methods. To address this issue, a purely unsupervised and generic two-stage architecture (named BERTCWS) for domain-specific Chinese word segmentation is proposed. We first design an incidence matrix termed the ‘character combination tightness’ to calculate the closeness between characters. Then, BERTCWS recognizes geoscience terms based on a Bidirectional Encoder Representations from Transformers(BERT)-based segmenter, and multi-granular segmentation is generated by setting different thresholds. Finally, the discriminator is constructed to validate the correctness of the segmented words. Our numerical study demonstrates that BERTCWS can identify both general-domain terms and geoscience-domain terms. Additionally, multi-granular segmentation could be applied to offer a set of potential geoscience terms of various lengths.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"16 1","pages":"387 - 399"},"PeriodicalIF":5.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88034653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-27DOI: 10.1080/19475683.2023.2183523
V. Sharma, Swagata Ghosh, S. Dey, Sultan Singh
ABSTRACT PM2.5 (Particulate matter with aerodynamic diameter <2.5 m) concentrations above permissible limit causes air quality deterioration and hampers human health. Due to the lack of a good spatial network of ground-based PM monitoring sites and systematic checking, the availability of continuous data of PM2.5 concentrations at macro and meso scales is restricted. Present research estimated PM2.5 concentrations at high (1 km) resolution over Faridabad, Ghaziabad, Gurugram and Gautam Buddha Nagar, a data-scarce zone of the highly urbanized area of northwestern India for the year 2019 using Random Forest (RF), Multi-Linear Regression (MLR) models and Hybrid Model combining RF and MLR. It included Aerosol Optical Depth (AOD), meteorological data and limited in-situ data of PM2.5. For validation, the correlation coefficient (R), Root-Mean-Square Error (RMSE), Mean Absolute Error (MAE) and Relative Prediction Error (RPE) have been utilized. The hybrid model estimated PM2.5 with a greater correlation (R = 0.865) and smaller RPE (22.41%) compared to standalone MLR/RF models. Despite the inadequate in-situ data, Greater Noida has been found to have a high correlation (R = 0.933) and low RPE (32.13%) in the hybrid model. The most polluted seasons of the year are winter (137.28 µgm−3) and post-monsoon (112.93 µgm−3), whereas the wet monsoon (44.56 µgm−3) season is the cleanest. The highest PM2.5 level was recorded in Noida followed by Ghaziabad, Greater Noida and Faridabad. The findings of the present research will provide an input dataset for air pollution exposure risk research in parts of northwestern India with sparse monitoring data.
{"title":"Modelling PM2.5 for Data-Scarce Zone of Northwestern India using Multi Linear Regression and Random Forest Approaches","authors":"V. Sharma, Swagata Ghosh, S. Dey, Sultan Singh","doi":"10.1080/19475683.2023.2183523","DOIUrl":"https://doi.org/10.1080/19475683.2023.2183523","url":null,"abstract":"ABSTRACT PM2.5 (Particulate matter with aerodynamic diameter <2.5 m) concentrations above permissible limit causes air quality deterioration and hampers human health. Due to the lack of a good spatial network of ground-based PM monitoring sites and systematic checking, the availability of continuous data of PM2.5 concentrations at macro and meso scales is restricted. Present research estimated PM2.5 concentrations at high (1 km) resolution over Faridabad, Ghaziabad, Gurugram and Gautam Buddha Nagar, a data-scarce zone of the highly urbanized area of northwestern India for the year 2019 using Random Forest (RF), Multi-Linear Regression (MLR) models and Hybrid Model combining RF and MLR. It included Aerosol Optical Depth (AOD), meteorological data and limited in-situ data of PM2.5. For validation, the correlation coefficient (R), Root-Mean-Square Error (RMSE), Mean Absolute Error (MAE) and Relative Prediction Error (RPE) have been utilized. The hybrid model estimated PM2.5 with a greater correlation (R = 0.865) and smaller RPE (22.41%) compared to standalone MLR/RF models. Despite the inadequate in-situ data, Greater Noida has been found to have a high correlation (R = 0.933) and low RPE (32.13%) in the hybrid model. The most polluted seasons of the year are winter (137.28 µgm−3) and post-monsoon (112.93 µgm−3), whereas the wet monsoon (44.56 µgm−3) season is the cleanest. The highest PM2.5 level was recorded in Noida followed by Ghaziabad, Greater Noida and Faridabad. The findings of the present research will provide an input dataset for air pollution exposure risk research in parts of northwestern India with sparse monitoring data.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"135 1","pages":"415 - 427"},"PeriodicalIF":5.0,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90243351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ABSTRACT The simulation of urban expansion has become an important means to assist urban development planning and ecological sustainable development. However, the spatial and temporal heterogeneities of urban expansion has been a major challenge for modelling urban expansion. This study designed three features from the perspective of spatiotemporal heterogeneity to improve the accuracy of CA model. The new features cover the trends effects of long time-series data on urban expansion, urban spatial growth intensity based on urban growth kernel estimation and allocation probability of the newly generated urban cells from global neighbourhood effects. Finally, urban expansion in Huizhou, China, was simulated and predicted. The experimental results show that the new features can effectively reduce the prediction error for the total amount of urban growth with a deviation of about 2%, and the overall accuracy of urban expansion is as high as 0.93. The features designed in this paper are shown to be effective and can be applied to urban simulations and scenario prediction with various models.
{"title":"Modelling urban expansion with cellular automata supported by urban growth intensity over time","authors":"Jinqu Zhang, Dong-Dong Wu, A-Xing Zhu, Yunqiang Zhu","doi":"10.1080/19475683.2023.2181393","DOIUrl":"https://doi.org/10.1080/19475683.2023.2181393","url":null,"abstract":"ABSTRACT The simulation of urban expansion has become an important means to assist urban development planning and ecological sustainable development. However, the spatial and temporal heterogeneities of urban expansion has been a major challenge for modelling urban expansion. This study designed three features from the perspective of spatiotemporal heterogeneity to improve the accuracy of CA model. The new features cover the trends effects of long time-series data on urban expansion, urban spatial growth intensity based on urban growth kernel estimation and allocation probability of the newly generated urban cells from global neighbourhood effects. Finally, urban expansion in Huizhou, China, was simulated and predicted. The experimental results show that the new features can effectively reduce the prediction error for the total amount of urban growth with a deviation of about 2%, and the overall accuracy of urban expansion is as high as 0.93. The features designed in this paper are shown to be effective and can be applied to urban simulations and scenario prediction with various models.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"1 1","pages":"337 - 353"},"PeriodicalIF":5.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88657178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-14DOI: 10.1080/19475683.2023.2166582
Hongxue Zeng, Xinghong Qu, Jing Cheng, Yunqiang Zhu, Li Peng
ABSTRACT Water wells are very important in the history of human development. The identification of spatiotemporal patterns of ancient water wells is a key to understanding the relationship between ancients and water, the evolution of ancient settlement patterns, and the history of population migration. However, at present, there are few reports to quantitatively explore the spatiotemporal evolution of ancient water wells from the perspective of geography. There is still a knowledge gap. Therefore, we show that a spatiotemporal kernel density estimation (STKDE) model and the centre-of-gravity method are useful for studying the spatiotemporal evolution of ancient water wells over the past 3,000 years in Zhejiang Province of southern China. The results show that in the past 3,000 years, the ancient water wells there have experienced an evolution from ‘single-core’ to ‘multicore’ aggregation, and the scope has gradually shifted from northern Zhejiang to southern Zhejiang. In addition, Hangzhou, Shangyu and ChunAn have always been aggregation centres. Socioeconomic factors, political and social stability, natural environmental conditions, population density and technological progress are the most important variables associated with water well ‘hot spots’ in time and space. This article provides a new perspective for the study of ancient water wells and fills the knowledge gap in understanding the spatiotemporal evolution characteristics of ancient water wells and other point or line features in the archaeological record.
{"title":"Spatiotemporal characteristics of the ancient water wells for the past 3,000a in Zhejiang Province, China: a perspective of geography","authors":"Hongxue Zeng, Xinghong Qu, Jing Cheng, Yunqiang Zhu, Li Peng","doi":"10.1080/19475683.2023.2166582","DOIUrl":"https://doi.org/10.1080/19475683.2023.2166582","url":null,"abstract":"ABSTRACT Water wells are very important in the history of human development. The identification of spatiotemporal patterns of ancient water wells is a key to understanding the relationship between ancients and water, the evolution of ancient settlement patterns, and the history of population migration. However, at present, there are few reports to quantitatively explore the spatiotemporal evolution of ancient water wells from the perspective of geography. There is still a knowledge gap. Therefore, we show that a spatiotemporal kernel density estimation (STKDE) model and the centre-of-gravity method are useful for studying the spatiotemporal evolution of ancient water wells over the past 3,000 years in Zhejiang Province of southern China. The results show that in the past 3,000 years, the ancient water wells there have experienced an evolution from ‘single-core’ to ‘multicore’ aggregation, and the scope has gradually shifted from northern Zhejiang to southern Zhejiang. In addition, Hangzhou, Shangyu and ChunAn have always been aggregation centres. Socioeconomic factors, political and social stability, natural environmental conditions, population density and technological progress are the most important variables associated with water well ‘hot spots’ in time and space. This article provides a new perspective for the study of ancient water wells and fills the knowledge gap in understanding the spatiotemporal evolution characteristics of ancient water wells and other point or line features in the archaeological record.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"27 1","pages":"307 - 318"},"PeriodicalIF":5.0,"publicationDate":"2023-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83668359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-10DOI: 10.1080/19475683.2023.2166583
D. Álvarez, María Teresa Camacho Olmedo
ABSTRACT CORINE Land Cover is one of the most relevant Land Use Cover (LUC) databases in Europe because of its degree of detail and long time series. Although some studies have assessed the uncertainty and inconsistencies of the database for specific years and periods, no work has been found that analyses all the available CORINE time series (1990–2018). In this study, we analyse the inconsistencies of the CORINE time series for a specific Spanish region (Asturias). To this end, we compare and analyse the CORINE status layers (CLC) and the CORINE layers of changes (CHA) for each of the mapped periods: 1990–2000, 2000–2006, 2006–2012, 2012–2018. Results show how CLC and CHA layers provide different information, especially after the change of production of CORINE Spain in 2012. The last two CORINE editions (2012, 2018) show a lot of technical changes that make the use of CLC layers very uncertain. In addition, mixed categories, whose definition is imprecise and, therefore, more uncertain, are behind most of detected change in both types of CORINE layers (CHA, CLC).
{"title":"Analysing the inconsistencies of CORINE status layers (CLC) and layers of changes (CHA) (1990-2018) for a Spanish case study","authors":"D. Álvarez, María Teresa Camacho Olmedo","doi":"10.1080/19475683.2023.2166583","DOIUrl":"https://doi.org/10.1080/19475683.2023.2166583","url":null,"abstract":"ABSTRACT CORINE Land Cover is one of the most relevant Land Use Cover (LUC) databases in Europe because of its degree of detail and long time series. Although some studies have assessed the uncertainty and inconsistencies of the database for specific years and periods, no work has been found that analyses all the available CORINE time series (1990–2018). In this study, we analyse the inconsistencies of the CORINE time series for a specific Spanish region (Asturias). To this end, we compare and analyse the CORINE status layers (CLC) and the CORINE layers of changes (CHA) for each of the mapped periods: 1990–2000, 2000–2006, 2006–2012, 2012–2018. Results show how CLC and CHA layers provide different information, especially after the change of production of CORINE Spain in 2012. The last two CORINE editions (2012, 2018) show a lot of technical changes that make the use of CLC layers very uncertain. In addition, mixed categories, whose definition is imprecise and, therefore, more uncertain, are behind most of detected change in both types of CORINE layers (CHA, CLC).","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"73 1","pages":"369 - 386"},"PeriodicalIF":5.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78563216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-07DOI: 10.1080/19475683.2023.2166108
I. Vlad, C. Díaz-Avalos, Pablo Juan, Somnath Chaudhuri
ABSTRACT The present research work is conducted to analyse spatial distribution and possible spatial association between three types of crimes from January 2018 to December 2019 in the metropolitan area of Mexico City. In this study, we consider treating the data as a realization of spatial point processes precisely on street network and propose an equal split continuous kernel estimator to identify particular street segments with higher crime rates than neighbouring segments. The results identify the location of high-risk areas for different kind of crimes and permit to detect individual street where crime rate is higher than the average rate. Additionally, our analysis reveals the existence of clusters with high crime incidence running eastwest across the central part of the urban study area. In that context, the current study suggests a comprehensive overview of road safety metrices for public security system and has important implications for strategic law enforcement. The methodology can be adapted and applied to other urban locations globally.
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